Psychiatry

Applications of Artificial Intelligence in Psychiatric Nursing: A Scope Review.

Rapid advances in artificial intelligence (AI) have reshaped healthcare, including psychiatric nursing, to address the limitations of traditional approaches and meet escalating mental health challenges. A scoping review analyzed 48 articles examining the application of AI in psychiatric nursing across different technologies and topics, noting trends in publications and countries involved. The arti...

Multimodal Machine Learning in Mental Health: A Survey of Data, Algorithms, and Challenges

The application of machine learning (ML) in detecting, diagnosing, and treating mental health disorders is garnering increasing attention. Traditionally, research has focused on single modalities, such as text from clinical notes, audio from speech samples, or video of interaction patterns. Recently, multimodal ML, which combines information from multiple modalities, has demonstrated significant...

Visual Stereotypes of Autism Spectrum in DALL-E, Stable Diffusion, SDXL, and Midjourney

Avoiding systemic discrimination requires investigating AI models' potential to propagate stereotypes resulting from the inherent biases of training...

PPCRKB: a risk factor knowledge base of postoperative pulmonary complications.

Postoperative pulmonary complications (PPCs) are highly heterogeneous disorders with diverse risk factors frequently occurring after surgical interven...

Exploring Implicit Biological Heterogeneity in ASD Diagnosis Using a Multi-Head Attention Graph Neural Network.

BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental disorder exhibiting heterogeneous characteristics in patients, including variabilit...

Hybrid Temporal Computing for Lower Power Hardware Accelerators

In this paper, we propose a new hybrid temporal computing (HTC) framework that leverages both pulse rate and temporal data encoding to design ultra-...

Leveraging LLMs to Predict Affective States via Smartphone Sensor Features

As mental health issues for young adults present a pressing public health concern, daily digital mood monitoring for early detection has become an i...

Machine learning for detection of heterogeneous effects of Medicaid coverage on depression.

In 2008, Oregon expanded its Medicaid program using a lottery, creating a rare opportunity to study the effects of Medicaid coverage using a randomize...

MDDBranchNet: A Deep Learning Model for Detecting Major Depressive Disorder Using ECG Signal.

Major depressive disorder (MDD) is a chronic mental illness which affects people's well-being and is often detected at a later stage of depression wit...

Neural substrates of predicting anhedonia symptoms in major depressive disorder via connectome-based modeling.

MAIN PROBLEM: Anhedonia is a critical diagnostic symptom of major depressive disorder (MDD), being associated with poor prognosis. Understanding the n...

Classification of Schizophrenia using Intrinsic Connectivity Networks and Incremental Boosting Convolution Neural Networks.

One of the key challenges in the use of resting brain functional magnetic resonance imaging (fMRI) network analysis for predicting mental illnesses su...

A Cross-Feature Mutual Learning Framework to Integrate Functional Connectivity and Activity for Brain Disorder Classification.

Time courses (TC) and functional network connectivity (FNC) features, derived from functional magnetic resonance imaging, show considerable potential ...

Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection.

Mental health conditions, prevalent across various demographics, necessitate efficient monitoring to mitigate their adverse impacts on life quality. T...

Data Quality Matters: Suicide Intention Detection on Social Media Posts Using RoBERTa-CNN.

Suicide remains a pressing global health concern, necessitating innovative approaches for early detection and intervention. This paper focuses on iden...

TAU-DI Net: A Multi-Scale Convolutional Network Combining Prob-Sparse Attention for EEG-based Depression Identification.

EEG-based detection of major depression disorder (MDD) plays a pivotal role in the subsequent treatment and recovery. With the rapid development of de...

Complexity Analysis based on Parietal Fuzzy Entropy to Facilitate ADHD Diagnosis in Young Children.

Attention deficit hyperactivity disorder (ADHD) is the most common condition affecting the development of neurons in children. Therefore, early and ac...

Evaluating Augmentation Approaches for Deep Learning-based Major Depressive Disorder Diagnosis with Raw Electroencephalogram Data.

While deep learning methods are increasingly applied in research contexts for neuropsychiatric disorder diagnosis, small dataset size limits their pot...

Label Noise-Robust Ensemble Deep Multimodal Framework For Neuroimaging Data.

Neuroimaging data have become widely studied in the context of identifying brain-based markers of mental illness. however, this work is hampered by th...

Exploring Self-Supervised Models for Depressive Disorder Detection: A Study on Speech Corpora.

Automatic detection of depressive disorder from speech signals can help improve medical diagnosis reliability. However, a significant challenge in thi...

Dynamic multi-hypergraph structure learning for disease diagnosis on multimodal data.

With its superior capability in complex data modeling, hypergraph computation is a powerful tool for many applications. In this work, we propose using...